Over the past decades, the over-reliance on herbicides during corn production has caused
severe environmental and biological problems such as pollution in the soil and underground
water, and the emergence of the herbicide-resistant weed species. A potential solution to reduce
the use of herbicides while maintaining adequate weed control lies in the combined use of
chemical and mechanical weeding, in which weeds are controlled adaptively according to their
reaction to herbicides. Accurate weed identification is a prerequisite for accomplishing such a
control strategy.
A machine vision system for weed identification, which utilized the morphological
properties of weed leaves, was developed in this research. The system incorporated a new
image segmentation algorithm, termed the ‘Pixelwise method’ to binarize the color weed
images for subsequent image processing and feature extraction procedures. Subsequently, a
Support Vector Machine (SVM) based classifier was constructed to distinguish various weed
species using seven morphological features.
2,325 indoor images consisting of six weed species were acquired during the first five
weeks after emergence of the plants. Among 1,006 test images, the SVM system achieved over
94% accuracy in crop (corn) versus weed discrimination and 95% in grass versus broadleaf
weed discrimination. The average classification accuracy for individual weed species was
approximately 86%. In addition, the system obtained the best classification result after the
second week after plant emergence. In field tests, the SVM classifier based on the indoor image
library was able to identify 71.1% of 270 weed plants in the field. With an adaptive median
filter to enhance the image quality, the accuracy was raised to 75.9% at the expense of extra
image processing time.
Both of the laboratory and field tests showed that the SVM method with reasonable
accuracy is feasible for weed identification during their early growth season.